Privacy Preserving Time-Series Forecasting of User Health Data Streams
Open accessAbstract
Privacy preservation plays a vital role in health care applications as the requirements for privacy preservation are very strict in this domain. With the rapid increase in the amount, quality and detail of health data being gathered with smart devices, new mechanisms are required that can cope with the challenges of large scale and real-time processing requirements. Federated learning (FL) is one of the conventional approaches that facilitate the training of AI models without access to the raw data. However, recent studies have shown that FL alone does not guarantee sufficient privacy. Differential privacy (DP) is a well-known approach for privacy guarantees, however, because of the noise addition, DP needs to make a trade-off between privacy and accuracy. In this work, we design and implement an end-to-end pipeline using DP and FL for the first time in the context of health data streams. We propose a clustering mechanism to leverage the similarities between users to improve the prediction accuracy as well as significantly reduce the model training time. Depending on the dataset and features, our predictions are no more than 0.025% far off the ground-truth value with respect to the range of value. Moreover, our clustering mechanism brings a significant reduction in the training time, with up to 49% reduction in prediction accuracy error in the best case, as compared to training a single model on the entire dataset. Our proposed privacy preserving mechanism at best introduces a decrease of ≈ 2% in the prediction accuracy of the trained models. Furthermore, our proposed clustering mechanism reduces the prediction error even in highly noisy settings by as much as 38% as compared to using a single federated private model.
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An end-to-end pipeline consisting of federated learning and differential privacy for health data streams using clustering reduces model training time with high accuracy, and prediction accuracy only decreases by 2% due to differential privacy.
“An end-to-end pipeline consisting of FL and DR is proposed for health data streams using a clustering mechanism to reduce model training time with high accuracy [31]. DP mechanism is used by the system to provide privacy guarantees and results showed that prediction accuracy only decreases by 2% for the trained model due to this change.”
From A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems (2023) · cites this paper as [31]
Differential privacy provides an effective mechanism to safeguard the exchange of ML model parameters from clients to FL or edge server.
“The differential privacy approach provides an effective mechanism to safeguard the exchange of ML model parameters from clients to FL or edge server [27,28,29,31].”
From A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems (2023) · cites this paper as [31]
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- Authors
- Imtiaz, S., Horchidan, S.-F., Abbas, Z., Arsalan, M., Chaudhry, H. N., & Vlassov, V.
- Affiliations
- KTH Royal Institute of Technology (SE)
- Otto-von-Guericke-Universität Magdeburg (DE)
- Politecnico di Milano (IT)
- Venue
- 2020 IEEE International Conference on Big Data (Big Data)
- Publisher
- IEEE
- Published
- 2020-12-10
- Pages
- 3428-3437
- OpenAlex
- W3138774967
- Cited by
- 15
- References
- 40
- Citations by year
- 2026: 3 · 2025: 1 · 2024: 2 · 2023: 4 · 2022: 2 · 2021: 3
- FWCI
- 0.86
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- IEEE International Conference on Big Data (BigData) (publishedVersion)
- Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)) (submittedVersion, other-oa)
- Funders
- European Commission
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Data Stream Mining Techniques
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Keywords
- privacy-preserving machine learning, federated learning, differential privacy, time series forecasting, privacy-accuracy trade-off, smart devices, real-time data processing
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